Government debt forecasting based on the Arima model
Open Access
- 17 January 2020
- journal article
- Published by LLC CPC Business Perspectives in Public and Municipal Finance
- Vol. 8 (1), 120-127
- https://doi.org/10.21511/pmf.08(1).2019.11
Abstract
The paper explores theoretical and practical aspects of forecasting the government debt in Ukraine. A visual analysis of changes in the amount of government debt was conducted, which has made it possible to conclude about the deepening of the debt crisis in the country. The autoregressive integrated moving average (ARIMA) is considered as the basic forecasting model; besides, the model work and its diagnostics are estimated. The EViews software package illustrates the procedure for forecasting the Ukrainian government debt for the ARIMA model: the series for stationarity was tested, the time series of monthly government debt was converted into stationary by making a number of transformations and determining model parameters; as a result, the most optimal specification for the ARIMA model was chosen.Based on the simulated time series, it is concluded that ARIMA tools can be used to predict the government debt values.Keywords
This publication has 5 references indexed in Scilit:
- How biased are U.S. government forecasts of the federal debt?International Journal of Forecasting, 2017
- How good are US government forecasts of the federal debt?International Journal of Forecasting, 2015
- Boundedness and nonlinearities in public debt dynamics: A TAR assessmentEconomic Modelling, 2013
- A Simple Stochastic Approach to Debt Sustainability Applied to LebanonIMF Working Papers, 2008
- Univariate Time-Series Analysis of Public DebtJournal of Quantitative Economics, 2004